Since roughly 1980, advances in computer technology, both hardware and software, and new techniques and advancements in chemistry and biology, have added additional levels of sophistication to the research done in biotechnology, drug discovery, and biomedical research. Many of the techniques developed are computational in nature. The vast increase in computational power and speed enables biological systems to be modeled, analyzed, and altered in great detail. The results of such computational modeling can then be utilized to design new drugs on a rational basis. This field of computational biological and chemical research is referred to herein as research done in silico. The adjective “in silico” thus refers to biochemical research wherein the system under investigation is mathematically modeled, modified, altered, and otherwise studied entirely within a computer-generated algorithmic model. The programming of the computer is based upon empirical knowledge of a host of chemical and physical parameters, such as three-dimensional geometry, bond lengths, bond strength, torsion, rotation, steric and electronic interactions, etc. In short, the amount of empirical biochemical data gathered to date greatly outstrips the conventional chemical and biochemical means of evaluating the significance of the collected data. “In silico” research thus designates the exploration and analysis of large quantities of empirical data by automatic and/or semi-automatic means in order to discover meaningful and predictive patterns and rules governing biological systems. The automatic and/or semi-automatic means for performing such research generally takes the form of a programmable computer and the input of the human programmer.
Traditionally, new drugs were found by isolating a molecule with a certain biological activity. This lead compound was then chemically modified using clues provided by a crude analysis of structure-activity relationships or by traditional medicinal chemistry techniques. The new, modified compounds were then synthesized and re-screened for the desired biological activity. This cycle continued until the desired biological activity of the compound was maximized. This approach, while effective, is very, very slow. A period of five or six years to bring a new drug to the preclinical phase is not uncommon using this traditional approach to drug discovery.
The in silico approach recognizes that many drugs are inhibitors of macromolecules (most often a protein, such as an enzyme or a proteinaceous receptor). Thus, a target molecule is chosen a priori, before any actual experiments are begun. The biological target is a macromolecule which is believed to be, or known to be, crucial for the biological activity or process which is to be inhibited. Of course, selecting a target for investigation is not always a simple process, especially when the biological activity to be inhibited is not parasitic in nature or when the number of possible targets is enormous.
Once a target has been selected, however, several technologies conventionally come into play using the in silico approach. As a general rule, the macromolecule to be studied is purified. An initial lead compound is then discovered by a variety of techniques such as high-throughput screening, where hundreds of thousands of compounds are examined en masse for binding to the purified target. Often, in a concurrent effort, the three-dimensional structure of the target macromolecule may be determined using nuclear magnetic resonance (NMR), X-ray crystallography, and other molecular modeling techniques. This data is collated and is used in designing the next series of compounds, which are then synthesized. This cycle is repeated until a compound is sufficiently potent, at which point it is sent to preclinical testing (on animals) and clinical testing (on humans). While faster that the conventional SAR aproach, in the current discovery cycle, an average time to reach preclinical investigation is still roughly three years.
Computer modeling of a biological system, of course,requires a good deal of empirical data, as well as theoretical models of macromolecular interactions. During the past 100 years, ligand binding has been described via two basic rationales. Emil Fisher first proposed in 1894 the “lock and key” rationale to describe ligand-receptor binding. Fischer (1894) Ber. dt. chem. Ges. 27:2985. In this model, the receptor (as used herein, the term “receptor” explicitly includes enzymes of all description, including ribozymes) is symbolized by a rigid lock into which the symbolic key, or ligand, must precisely fit. This was the sole model used to describe ligand binding events for over 50 years. In 1958, Koshland proposed an “induced fit” model to describe ligand-receptor binding events that seemed to proceed in a zipper-like fashion. Koshland (1958) Proc. Natl. Acad. Sci. U.S.A. 44:98. He hypothesized that binding of the substrate “causes a change in the 3-dimensional relationship in the active site.” It is this change that then leads to a fit that occurs only after the changes induced by ligand binding. Over the years, conformational changes of the receptor ascribed to an “induced fit” binding have ranged from very subtle movements of amino acid side chains to large conformational changes involving movements of entire protein domains.
These two complementary models have been utilized to describe most of the structural data presently available in the literature. The “lock and key” rationale describes the binding event if, after inspection of the ligand-receptor complex, the observed receptor conformation resembles the unbound-receptor conformation. Conversely, if the conformation of the bound-receptor is different than the unbound-receptor (no matter how subtle the differences), then the “induced fit” model rationalizes the observed ligand-binding process.
Recently, “stabilization of receptor conformational ensembles” has emerged to rationalize a range of ligand binding events without necessitating either the “lock and key” or “induced fit” mechanisms. See Kumar et al. (1999) Cell Biochem. and Biophys. 31:141-164; Ma et al. (1999) Protein Eng. 12:713-720; Tsai et al. (1999) Protein Sci. 8:1181-1190. This model assumes that macromolecules exist in solution as multiple, equilibrating conformations. These various conformations can be described by mechanical laws, using standard statistical distributions. The process of ligands binding to the receptors thus shifts the equilibrium from the statistical distribution of native conformations when the ligand is absent, to a new equilibrium that includes the receptor-ligand conformation. In this view, ligands bind to an ensemble of pre-existing receptor conformations. Ligand binding then shifts the overall dynamic equilibrium to stabilize the conformation present in the receptor-ligand complex.
This concept of conformationally mobile receptors (and ligands) is not new, but arose shortly after the discovery of modern conformational analysis. A paper published in 1964 states that “the conformation of an enzyme in solution is regarded to be a statistical average of a number of conformations, the protein structure oscillating between these conformations.” Straub (1964) Advan. Enzymol. 26:89-114. Since then, the conformational mobility of biologically active proteins has been repeatedly demonstrated via biophysical methods.
Nevertheless, due to computational limitations, current molecular modeling and drug design efforts treat proteins as static models even though they are clearly dynamic macromolecular structures, constantly in motion. In general, the static models portray either the native protein conformation or the protein conformation tightly bound to a potent peptide-derived inhibitor. Some modeling studies accommodate small changes in protein and ligand side chain conformations or hydrogen bonding interactions. This approach, called the “soft lock and key” model has subsequently been utilized to modify inhibitor design. Sowdhamini et al. (1995) Pharm. Acta Helv. 69:185-192.
But other (and significantly altered) protein conformations are not considered when designing or modifying enzyme inhibitors, in spite of the fact that biophysical methods have established their existence. Thus, there remains a long-felt and unmet need to explore these conformations as a means to yield information that leads to the rational design of targeted, biologically-active compounds.